Visual cohort comparison for spatial single-cell omics-data
This addresses the need for explorative data analysis in large-scale clinical cohort studies with spatially-resolved omics-data, though it appears incremental as it builds on existing visualization techniques for a specific domain.
The researchers tackled the challenge of comparing cohorts of spatially-resolved omics-data for disease understanding and biomarker identification by developing an interactive visual analysis workflow, which enables identification of cohort-differentiating features and outlier samples across multiple levels of detail, as demonstrated through case studies with domain experts.
Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow we conducted multiple case studies with domain experts from different application areas and with different data modalities.